AI due diligence tests a target’s claims about artificial intelligence against evidence of real use, data permissions and operating controls. Public material frames the questions; authorized review of systems, contracts and workflow records establishes what is running. The findings distinguish supported claims from open questions before a deal team relies on them.
In short
- Separate using AI to support a review from evaluating a target that claims to use AI.
- A demonstration or public product description does not establish production use, quality or economics.
- Request authorized workflow, evaluation, data and operating evidence, with owners for unresolved questions.
How is AI used in due diligence?
AI can help a diligence team organize documents, extract candidate facts and identify questions for review. AI due diligence also means examining a target's own AI claims: what runs in production, how it is evaluated and which people remain accountable for its outputs.
AI due diligence is a scoped review of a target's AI claims, dependencies and operating evidence before an investment or implementation decision.
AI due diligence in private equity belongs within technology due diligence. Generated summaries and public product claims need validation against source evidence before they support a finding.
- Keep each extracted claim linked to the underlying source and its access boundary.
- Have responsible reviewers validate material conclusions and unresolved contradictions.
- Use agreed data handling arrangements before sensitive deal information enters an AI tool.
- Separate a plausible use case from a demonstrated production capability.
Who conducts due diligence?
The sponsor coordinates due diligence with management and specialists responsible for the deal questions. An AI review needs business owners who understand the workflow and technical reviewers who can evaluate systems, data and operating evidence; counsel resolves legal questions about rights, contracts and privacy.
Agree who can authorize access, explain the claimed capability and accept the resulting action plan. The tech stack and technical debt assessment maps surrounding dependencies, while the privacy compliance audit organizes tracking, notices and consent evidence for counsel.
What evidence shows a target's AI is in production?
Production evidence connects an AI capability to an active business workflow, its accountable owner and dated operating records. Authorized deployment history, usage records, evaluations, incident handling and fallback procedures can test the claim; a polished demonstration alone cannot establish reliability or business value.
Record public AI claims and the customer-facing workflow each claim describes.
Identify the deployed capability, business owner, intended users and actual workflow.
Review deployment history, authorized usage records and representative output evaluations.
Inspect model and vendor dependencies, costs, operating support and fallback arrangements.
Request data handling, permission and contract records; refer legal conclusions to counsel.
Compare claimed benefits with the agreed baseline and counting rule, retaining unsupported claims as questions.
Source: Meo Advisors checklist. As of October 2026.
Use the IT due diligence checklist for the broader evidence inventory. Do not infer production quality or savings from a vendor badge, hiring notice or demonstration.
What can be checked publicly, and what needs authorized access?
Public pages can show a target's claims, visible applications and some vendor signals. The AI implementation, private datasets, evaluations, usage economics and control records need authorized access; public observations provide questions for that review.
| Visible from outside | Needs authorized access |
|---|---|
| Front end and templates | App internals |
| Tags and measurement | Data integrations |
| Customer-facing apps | Code and technical debt |
| Domains and hosting | Vendor contracts and SaaS spend |
Before authorization we use passive public sources only: DNS, certificate transparency, third-party internet-scan datasets, and the target’s public pages at normal browsing rates. No port scans, vulnerability scans or login attempts without written authorization.
Define the records and permitted methods before the review begins. Any check inside the target's systems runs only on request with authorization, using agreed handling for sensitive information. The IT due diligence guide connects those evidence requests to the wider acquisition review.
Which mistakes weaken an AI diligence conclusion?
An AI diligence conclusion is weakened when a team treats a demonstration as production evidence, accepts a vendor claim as a measured result or omits the cost and ownership of operating the workflow. The review should make each assumption and missing record explicit.
- Using selected outputs without testing representative tasks and known failure conditions.
- Treating data access as evidence that the company has the required rights to use it.
- Reporting benefits without the baseline, counting rule or ongoing operating costs.
- Leaving human review, fallback handling and responsibility for changes unassigned.
How does AI due diligence inform the post-close plan?
AI due diligence informs a post-close plan by separating supported production capabilities, unresolved evidence requests and proposed opportunities. The sponsor can then agree what to retain, investigate or pilot, with owners and verification steps before committing to a wider rollout.
The AI strategy guide turns supported opportunities into priorities. The AI in private equity guide connects deal work to portfolio implementation, and AI readiness assessment checks whether a proposed workflow has the evidence and ownership to proceed.
See the sample diligence deliverables for the evidence, open-question and action formats.
Meo Advisors Editorial writes and maintains this page. Its numbers are Meo Advisors’ own statements or cited external benchmarks, and every mock is labelled Illustrative. See the editorial policy and corrections process.